This repository serves as a comprehensive, curated list of Integrated Circuits (ICs) and Intellectual Property (IP) cores specifically designed for Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) applications. It targets engineers, researchers, and industry professionals seeking an overview of the AI hardware landscape, providing a valuable resource for understanding the current state and emerging trends in AI chip development.

How It Works

The project meticulously compiles and categorizes information on AI hardware from various sources, including major tech vendors, established IP providers, and emerging startups. It details specific chip architectures, their intended applications (training, inference, edge, cloud), key performance metrics, and notable technological advancements, offering a structured view of the rapidly evolving AI hardware ecosystem.

Quick Start & Requirements

This is a static, curated list. No installation or execution is required. The primary purpose is informational.

Highlighted Details

• Extensive coverage of major IC vendors (Intel, NVIDIA, Qualcomm, AMD, IBM, Marvell) and tech giants (Google, AWS, Microsoft, Apple).
• Detailed listings of numerous AI chip startups, including funding rounds and specific product announcements.
• Includes information on IP vendors (ARM, Synopsys, CEVA) and their AI-focused offerings.
• References to key AI chip benchmarks like MLPerf and DAWNBench are provided.

Maintenance & Community

The repository appears to be actively updated with recent news and product releases in the AI hardware space, indicated by frequent "Latest updates" entries. Community interaction points are not explicitly mentioned in the README.

Licensing & Compatibility

The repository itself is a list of information and does not have a specific license mentioned for the content. It is intended for informational purposes.

Limitations & Caveats

The README is a dense, unformatted collection of news snippets and product announcements, making it challenging to extract structured data without significant parsing effort. It lacks a clear, standardized format for each entry, and some information may be outdated due to the rapid pace of the AI hardware industry.